Effectiveness of workplace choice architecture modification for healthy eating and daily physical activity

Background Modifying the choice architecture of behavioural contexts can facilitate health behaviour change, but existing evidence builds mostly on small-scale interventions limited in duration, targets, strategies, and settings. We evaluated the effectiveness of a one-year hybrid type 2 implementation-effectiveness trial aimed at promoting healthy eating and daily physical activity with subtle modifications to the choice architecture of heterogeneous worksites. The intervention was contextualised to and integrated into the routine operations of each worksite. Effectiveness was evaluated in a quasi-experimental pre-post design. Methods Intervention sites (n = 21) implemented a median of two (range 1–9) intervention strategies for healthy eating and one (range 1–5) for physical activity. Questionnaires pre (n = 1126) and post (n = 943) intervention surveyed employees’ behavioural patterns at work (food consumption: vegetables/roots, fruit/berries, nuts/almonds/seeds, sweet treats, fast food, water; physical activity: restorative movement, exercise equipment use, stair use). The post-intervention questionnaire also measured employees’ perception of and response to three intervention strategies: a packed lunch recipe campaign, a fruit crew-strategy, and movement prompts. Multi- and single-level regression models evaluated effectiveness, treating intervention as a continuous predictor formed of the site-specific dose (n intervention strategies employed) and mean quality (three-point rating per strategy halfway and at the end of the intervention) of implementation relevant to each outcome. Results Multinomial logistic regression models found the intervention significantly associated with a favourable change in employees’ fruit and berry consumption (interaction effect of time and implementation p = 0.006) and with an unfavourable change in sweet treat consumption (p = 0.048). The evidence was strongest for the finding concerning fruit/berry consumption—an outcome that sites with greater dose and quality of implementation targeted by using strategies that reduced the physical effort required to have fruit/berries at work and by covering multiple eating-related contexts at the worksite. The quality of implementation was positively associated with the perception of (p = 0.044) and response to (p = 0.017) the packed lunch recipes, and with response to the fruit crew-strategy (p < 0.001). Conclusions The results suggest that a contextualised, multicomponent choice architecture intervention can positively influence eating behaviour in diverse real-world settings over a one-year period, and that higher implementation quality can enhance intervention perception and response. However, outcomes may depend on the type of intervention strategies used and the extent of their delivery. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-024-18482-1.

The table shows the mean implementation quality ratings of corresponding intervention strategies with values rounded to one decimal place.The mean values represent the average of individual strategies whose quality was rated at two time points, halfway through and at the end of the intervention.
2 Worksite cafeteria involved in the intervention.
The table shows the mean implementation quality ratings of corresponding intervention strategies with values rounded to one decimal place.The mean values represent the average of individual strategies whose quality was rated at two time points, halfway through and at the end of the intervention.
2 Worksite cafeteria involved in the intervention.

Statistical analyses
We studied the effectiveness of the StopDia at Work-intervention on the defined outcomes with mixed-effects models and conventional regression models.Mixed-effects models were specified with a 2-level data structure using site (n=21) as the clustering variable.We built linear mixed models for continuous outcomes and generalised linear mixed models for categorical outcomes, respectively, with the MIXED and GENLINMIXED routines of IBM SPSS statistics ® version 29.0 (IBM Corp., Armonk, NY, USA).The default estimation method SPSS employs is restricted maximum likelihood (REML) in MIXED (Heck et al., 2021, p. 20) and a quasilikelihood approach called active set method (ASM) with Newton-Raphson estimation in GENLINMIXED (Heck et al., 2012, p. 27).We included random intercept as the random effect and selected variance components as the covariance structure for the random coefficients.We selected the Satterthwaite approximation to the degrees of freedom that were used to compute significance tests for model parameters, as recommended for data with varying number of individuals across clusters (Heck et al., 2012, p. 147).In the generalised linear mixed models for categorical outcomes, we additionally selected a robust, more conservative approach to the calculation of the standard errors of regression coefficients to allow departures from normality.Conventional single-level logistic regression models were built with the IBM SPSS NOMREG procedure for multinomial outcomes and with the IBM SPSS GENLIN procedure for dichotomous outcomes.Both procedures employ maximum likelihood estimation (Heck et al., 2012, p. 27).
For all outcome variables, we fitted first an intermediate model that included the primary predictor of our interest and then a final model that was adjusted for relevant covariates.As we used sites as observational units, independent variables included in the models were summarised to the site level to reflect site-level properties.The summarising concerned the following individual-level variables: physical work, a habit of eating at the worksite cafeteria, wish for support in healthy eating/physical activity, and the completion of the questionnaire both pre and post intervention.The summarising involved computing the proportion of individuals with the desired characteristic (e.g., physical work) per site and timepoint, and assigning the resulting values to the individual respondents of the corresponding site and time.The summarised variables were additionally grand-mean centred within the dataset that was included in the analysis by subtracting the overall sample mean from the site-level value.Grand-mean-centring recentres the site's standing on the variable against the sample mean and facilitates the interpretation of the coefficients of model parameters (Heck et al., 2012, p. 21).

Results
Table S5.Reasons for never performing restorative movements or using available exercise equipment.

Table S1 .
Characteristics of intervention sites and questionnaire data collected pre and post intervention.

Table S2 .
(Lindström et al., 2021)ore variable based on the Healthy Diet Index(Lindström et al., 2021) .g., move at home, no need, medical reason, physical work, work clothes. 2 e.g., no need, medical reason, move after work, use breaks for eating, use break exercise application, prefer moving without equipment, laziness, heavy work clothing, pregnancy, does not feel good/useful, work community's objection, don't get around to using the equipment alone.